With the ever increasing number of vehicles on the roadways, there is a need for improved and more efficient traffic management. One aspect of traffic management is identifying when a vehicle is present and classifying the vehicle. Since the 1950s, point detection devices, such as in-ground inductive loops, have primarily been used for intersection control and traffic data collection. The in-ground inductive loops basically utilize wire loops placed in the pavement, for detecting the presence of vehicles through magnetic induction. Many limitations exist with point detection devices such as inductive loops. For example, inductive loops are expensive to install, difficult to maintain, and cannot classify vehicles. More recent traffic sensor systems use radar, active audio, such as ultrasound beams, or video to detect or classify vehicles. These systems can not only be used as detection devices for highway monitoring but also can provide more detailed information from a traffic scene. While active audio systems utilizing transmission and detection of audio signals have been used, passive audio systems have not yet been widely developed. Passive audio systems can utilize relatively inexpensive sensing devices while potentially covering a wide area. Further, passive audio systems are insensitive to weather and light conditions.
Classification type neural networks can be used to recognize and classify input patterns based on example patterns used to train the networks during a training phase. Neural networks are systems that are deliberately constructed to make use of some of the organizational principles from the human brain. Neural networks use a highly parallel set of connections of simple computational devices to produce a classification of an input pattern. While neural networks originally were implemented using hardware, software implementations are now very common. The overall behavior of a neural network is determined by the structure and strengths of the adjustable connections, the synaptic weights, between the computational elements. The synaptic weights are adjusted during the training phase, when examples representative of those the network must ultimately classify and their correct classifications are provided to the network. After the training phase, the network is conditioned to respond uniquely to a particular input signal to provide a desired output signal. Neural networks are particularly suited for use in systems where speed and accuracy in pattern recognition is desired.